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Programvaruhandledning

Generate a Hybrid Mesh by Combining Block Ranger and GVol

This tutorial will demonstrate a method to create a hybrid mesh of tetrahedral zones to model the rock mass and hexahedral zones to model a concrete liner. Hexahedral zones for the liner are preferred in order to more accurately capture plastic strains in this region. The meshing is done by utilizing the Itasca Griddle volume mesher plug-in for Rhino 3D. Importing the final mesh into FLAC3D, for future finite volume modeling, is also demonstrated.

FLAC3D 6.0 Model Generation using the Building Blocks and Geometric Data Sets

MINEDW Tutorial (Part 4: Meshing)

In this tutorial we will go over meshing, from the creation of a 2D mesh and how to import it to MINEDW, to the inclusion of topography, layers, and pinch-outs to different areas of interest in the model.

Artiklar och presentationer

Analysis of Large-ScalePit Slope Stability —The Aitik Mine Revisited

Flowback Test Analyses at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) Site

Injection testing conducted in 2017 and 2019 at the Frontier Observatory for Research in Geothermal Energy site in Utah evaluated flowback as an alternative to prolonged shut-in periods to infer closure stress, formation compressibility, and formation permeability. Flowback analyses yielded lower inferred closure stresses than traditional shut-in methods and indicated high formation compressibility, suggesting an extensive fractured system. Numerical simulations showed rebound pressure is not necessarily the lower bound of minimum principal stress. Stiffness changes can be identified as depletion transitions from hydraulic to natural fractures. The advantage if flowback is reduced time to closure.

Blast Movement Simulation Through a Hybrid Approach of Continuum, Discontinuum, and Machine Learning Modeling

This work presents a hybrid modeling approach to efficiently estimate and optimize rock movement during blasting. A small-scale continuum model simulates early-stage, near-field blasting physics and generates synthetic data to train a machine learning (ML) model. Key parameters such as expanded hole diameter, burden velocity, and gas pressure are obtained through the ML model, which then inform a discontinuum model to predict far-field muckpile formation. The approach captures essential blast physics while significantly accelerating blast design optimization.